Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:210384
loss:CategoricalContrastiveLoss
Instructions to use Detomo/cl-nagoya-sup-simcse-ja-nss-v_1_0_7_10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Detomo/cl-nagoya-sup-simcse-ja-nss-v_1_0_7_10 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Detomo/cl-nagoya-sup-simcse-ja-nss-v_1_0_7_10") sentences = [ "科目:コンクリート。名称:基礎部コンクリート打設手間。", "科目:コンクリート。名称:コンクリート打設手間・ポンプ圧送。", "科目:コンクリート。名称:普通コンクリート。摘要:FC=24 S15粗骨材基礎部。備考:代価表 0059。", "科目:コンクリート。名称:基礎部コンクリート。摘要:FC36N/mm2 スランプ18高性能AE減水剤。備考:代価表 0032。" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9b0bc361dd967ecacb49d5a971f9ce33fe553b17e9191e7388a20010d126e384
- Size of remote file:
- 445 MB
- SHA256:
- e56a51446c139bff5bb450bc708b1516c9e6d6d6993eead0bd96580d4ea83340
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